Setup

# devtools::install_local("geneRefineR/", force = T)
library("geneRefineR") 

library(readxl)
library(DT)
library(data.table)
library(dplyr)
library(ggplot2)
library(plotly)
library(cowplot)
library(ggrepel)
library(curl)
library(biomaRt)
library(sqldf)
# Ensembl LD API
library(httr)
library(jsonlite)
library(xml2)
library(gaston)
library(RCurl)

# *** susieR ****
# library(knitrBootstrap) #install_github('jimhester/knitrBootstrap')
library(susieR) # devtools::install_github("stephenslab/susieR")


sessionInfo()
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14.3
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] susieR_0.6.2.0411      RCurl_1.95-4.11        bitops_1.0-6          
##  [4] gaston_1.5.4           RcppParallel_4.4.2     Rcpp_1.0.0            
##  [7] xml2_1.2.0             jsonlite_1.6           httr_1.4.0            
## [10] sqldf_0.4-11           RSQLite_2.1.1          gsubfn_0.7            
## [13] proto_1.0.0            biomaRt_2.38.0         curl_3.3              
## [16] ggrepel_0.8.0          cowplot_0.9.4          plotly_4.8.0          
## [19] ggplot2_3.1.0          dplyr_0.8.0.1          data.table_1.12.0     
## [22] DT_0.5.2               readxl_1.3.0           geneRefineR_0.0.0.9000
## 
## loaded via a namespace (and not attached):
##  [1] lattice_0.20-38      tidyr_0.8.2          prettyunits_1.0.2   
##  [4] assertthat_0.2.0     digest_0.6.18        R6_2.4.0            
##  [7] cellranger_1.1.0     plyr_1.8.4           chron_2.3-53        
## [10] stats4_3.5.1         evaluate_0.13        pillar_1.3.1        
## [13] rlang_0.3.1          progress_1.2.0       lazyeval_0.2.1      
## [16] blob_1.1.1           S4Vectors_0.20.1     Matrix_1.2-15       
## [19] rmarkdown_1.11       stringr_1.4.0        htmlwidgets_1.3     
## [22] bit_1.1-14           munsell_0.5.0        compiler_3.5.1      
## [25] xfun_0.5             pkgconfig_2.0.2      BiocGenerics_0.28.0 
## [28] htmltools_0.3.6      tcltk_3.5.1          tidyselect_0.2.5    
## [31] expm_0.999-3         tibble_2.0.1         IRanges_2.16.0      
## [34] XML_3.98-1.17        viridisLite_0.3.0    crayon_1.3.4        
## [37] withr_2.1.2          grid_3.5.1           gtable_0.2.0        
## [40] DBI_1.0.0            magrittr_1.5         scales_1.0.0        
## [43] stringi_1.3.1        tools_3.5.1          bit64_0.9-7         
## [46] Biobase_2.42.0       glue_1.3.0           purrr_0.3.0         
## [49] hms_0.4.2            parallel_3.5.1       yaml_2.2.0          
## [52] AnnotationDbi_1.44.0 colorspace_1.4-0     memoise_1.1.0       
## [55] knitr_1.21
print(paste("susieR ", packageVersion("susieR")))
## [1] "susieR  0.6.2.411"

Finemap PD Genes

list[top_SNPs, SumStats_sig] <- import_sig_GWAS(
  file_path = "Data/Parkinsons/Nalls2018_S2_SummaryStats.xlsx",
  sheet="Data",
  chrom_col = "CHR", position_col = "BP", snp_col="SNP",
  pval_col="P, all studies", effect_col="Beta, all studies", gene_col="Nearest Gene",
  caption= "Nalls et al. (2018) PD GWAS Summary Stats")


finemapped_PD <- finemap_geneList(top_SNPs, geneList=c("LRRK2","GBAP1","SNCA","VPS13C","GCH1"), #unique(top_SNPs$Gene) 
                 filePath="Data/Parkinsons/META.PD.NALLS2014.PRS.tsv",
                  snp_col = "MarkerName", pval_col = "P.value")

LRRK2

  • Extracting SNPs flanking lead SNP…

  • Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.

  • Fine mapping with SusieR… [1] “objective:-4890.90196469039” [1] “objective:-4890.86512255644” [1] “objective:-4890.86493793192” [1] “objective:-4890.86493700424”

## Warning in log(susieDF$Effect): NaNs produced

## Warning in log(susieDF$Effect): NaNs produced
## Warning: Removed 1 rows containing missing values (geom_hline).

1 / 10 (10%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.

GBAP1

  • Extracting SNPs flanking lead SNP…

  • Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.

  • Fine mapping with SusieR… [1] “objective:-1768.43682307539” [1] “objective:-1767.99485312494” [1] “objective:-1767.99377794051” [1] “objective:-1767.99377530418” [1] “objective:-1767.99377529783” [1] “objective:-1767.99377529781”

## Warning in log(susieDF$Effect): NaNs produced
## Warning in log(susieDF$Effect): NaNs produced
## Warning: Removed 1 rows containing missing values (geom_hline).

0 / 10 (0%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.

SNCA

  • Extracting SNPs flanking lead SNP…

  • Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.

  • Fine mapping with SusieR… [1] “objective:-3587.57986639256” [1] “objective:-3584.84439532171” [1] “objective:-3584.84224690147” [1] “objective:-3584.84224525982” [1] “objective:-3584.84224525865” [1] “objective:-3584.84224525865”

## Warning in log(susieDF$Effect): NaNs produced
## Warning in log(susieDF$Effect): NaNs produced

2 / 10 (20%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.

VPS13C

  • Extracting SNPs flanking lead SNP…

  • Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.

  • Fine mapping with SusieR… [1] “objective:-3928.09335227999” [1] “objective:-3928.05140701022” [1] “objective:-3928.05134302287” [1] “objective:-3928.05134292561”

## Warning in log(susieDF$Effect): NaNs produced

## Warning in log(susieDF$Effect): NaNs produced
## Warning: Removed 1 rows containing missing values (geom_hline).

1 / 10 (10%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.

GCH1

  • Extracting SNPs flanking lead SNP…

  • Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.

  • Fine mapping with SusieR… [1] “objective:-2952.66209303207” [1] “objective:-2952.62332682449” [1] “objective:-2952.62312855485” [1] “objective:-2952.62312754298”

## Warning in log(susieDF$Effect): NaNs produced
## Warning in log(susieDF$Effect): NaNs produced
## Warning: Removed 1 rows containing missing values (geom_hline).

0 / 10 (0%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.

Finemap AD Genes

list[top_SNPs, SumStats_sig] <- import_sig_GWAS(
  file_path = "Data/Alzheimers/Posthuma/AD_target_SNP.xlsx",
  sheet = 3,
  chrom_col = "Chr", position_col = "bp", snp_col="SNP",
  pval_col="P", effect_col="Z", gene_col="Gene",
  caption= "Posthuma et al. (2018) AD GWAS Summary Stats")


finemapped_AD <- finemap_geneList(top_SNPs, geneList=c("CLU/PTK2B","APOE"), #unique(top_SNPs$Gene) 
                 filePath="Data/Alzheimers/Posthuma/phase3.beta.se.hrc.txt",
                  effect_col = "BETA", stderr_col = "SE", position_col = "BP")

CLU/PTK2B

  • Extracting SNPs flanking lead SNP…
## Warning: NAs introduced by coercion

## Warning: NAs introduced by coercion
  • Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.

  • Fine mapping with SusieR… [1] “objective:-6159.30949844661” [1] “objective:-6158.9541676718” [1] “objective:-6158.95404180037” [1] “objective:-6158.95404175575”

## Warning in log(susieDF$Effect): NaNs produced
## Warning in log(susieDF$Effect): NaNs produced

1 / 10 (10%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.

APOE

  • Extracting SNPs flanking lead SNP…
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion

+ Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.

  • Fine mapping with SusieR… [1] “objective:-4950.18491679679” [1] “objective:-4721.83314537539” [1] “objective:-4721.71980975089” [1] “objective:-4721.71975448281” [1] “objective:-4721.71975446467” [1] “objective:-4721.71975446467”
## Warning in log(susieDF$Effect): NaNs produced
## Warning in log(susieDF$Effect): NaNs produced

0 / 10 (0%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.